if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
r = True
models = [
PowerBeutler2017(recon=r, smooth_type="hinton2017", name="Hinton2017"),
PowerBeutler2017(recon=r, smooth_type="eh1998", name="EH1998")
]
data = PowerSpectrum_SDSS_DR12_Z061_NGC(name="Recon mean",
recon=r,
min_k=0.02,
max_k=0.30)
sampler = DynestySampler(temp_dir=dir_name)
fitter = Fitter(dir_name)
fitter.add_model_and_dataset(models[0], data, name="Hinton2017")
fitter.add_model_and_dataset(models[1], data, name="EH1998")
fitter.set_sampler(sampler)
fitter.set_num_walkers(10)
fitter.fit(file)
if fitter.should_plot():
from chainconsumer import ChainConsumer
c = ChainConsumer()
pks = {}
for posterior, weight, chain, model, data, extra in fitter.load():
c.add_chain(chain,
weights=weight,
parameters=model.get_labels(),
import sys
sys.path.append("..")
from barry.samplers import DynestySampler
from barry.cosmology.camb_generator import getCambGenerator
from barry.postprocessing import BAOExtractor
from barry.config import setup
from barry.models import PowerSeo2016, PowerBeutler2017, PowerDing2018, PowerNoda2019
from barry.datasets import PowerSpectrum_SDSS_DR12_Z061_NGC
from barry.fitter import Fitter
import numpy as np
import pandas as pd
if __name__ == "__main__":
pfn, dir_name, file = setup("../config/pk_individual.py")
fitter = Fitter(dir_name, save_dims=2, remove_output=False)
c = getCambGenerator()
r_s = c.get_data()[0]
p = BAOExtractor(r_s)
sampler = DynestySampler(temp_dir=dir_name, nlive=200)
for r in [True, False]:
t = "Recon" if r else "Prerecon"
ls = "-" if r else "--"
d = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r, realisation=0)
de = PowerSpectrum_SDSS_DR12_Z061_NGC(recon=r,
postprocess=p,
realisation=0)
import os
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
sys.path.append("..")
from barry.config import setup
from barry.models import CorrBeutler2017, CorrDing2018, CorrSeo2016
from barry.datasets import CorrelationFunction_SDSS_DR12_Z061_NGC
from barry.samplers import DynestySampler
from barry.fitter import Fitter
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
sampler = DynestySampler(temp_dir=dir_name, nlive=1000)
fitter = Fitter(dir_name, remove_output=False)
cs = ["#262232", "#116A71", "#48AB75", "#D1E05B"]
for r in [True, False]:
t = "Recon" if r else "Prerecon"
ls = "-" if r else "--"
d = CorrelationFunction_SDSS_DR12_Z061_NGC(recon=r)
# Fix sigma_nl for one of the Beutler models
model = CorrBeutler2017()
sigma_nl = 6.0 if r else 9.3
model.set_default("sigma_nl", sigma_nl)
model.set_fix_params(["om", "sigma_nl"])
fitter.add_model_and_dataset(CorrBeutler2017(), d, name=f"Beutler 2017 {t}", linestyle=ls, color=cs[0])
fitter.add_model_and_dataset(model, d, name=f"Beutler 2017 Fixed $\\Sigma_{{nl}}$ {t}", linestyle=ls, color=cs[0])
sys.path.append("../..")
from barry.samplers import DynestySampler
from barry.config import setup
from barry.models import PowerBeutler2017
from barry.datasets.dataset_power_spectrum import PowerSpectrum_DESIMockChallenge_Post
from barry.fitter import Fitter
import numpy as np
import pandas as pd
from barry.models.model import Correction
from barry.utils import weighted_avg_and_cov, break_vector_and_get_blocks
import matplotlib as plt
from matplotlib import cm
if __name__ == "__main__":
pfn, dir_name, file = setup(__file__)
fitter = Fitter(dir_name, remove_output=True)
sampler = DynestySampler(temp_dir=dir_name, nlive=500)
names = [
"PostRecon Yuyu NonFix ",
"PostRecon Yuyu NonFix ",
]
cmap = plt.cm.get_cmap("viridis")
smoothtypes = [1, 2, 3, 4] # [5, 10, 15, 20] Mpc/h
kmaxs = [0.15, 0.20, 0.25, 0.30]
allnames = []
counter = 0
fit_poles = [0, 2, 4]